Interactive Visualizations are powerful these days because those are all made for web. Web – simply a combination of
apexcharter which is developed by dreamRs – Victor Perrier and Team to make beautiful interactive visualizations that are based on
apexcharter – Intro, Installation & Loading
apexcharter is built as a htmlwidget (R Package) for
apexchart.js and the API design is inspired by
apexcharter requires RStudio >= 1.2 to properly display charts.
Install the stable version from CRAN with:
Or install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("dreamRs/apexcharter")
Once successfully installed,
apexcharter can be loaded using
The main function of
apexcharter is the
apex() function whose first argument is
data. Thus, enabling the support of pipe
%>% operator. The second argument is
mapping – aesthetics (x & y) and the third one is
type of the chart – which takes multiple values like
line and much more.
Let’s take R’s in-built
mtcars dataset and draw a simple bar chart.
library(apexcharter) library(tidyverse) mtcars %>% count(cyl) %>% apex(type = "bar", mapping = aes(x = "cyl", y = n))
Now, that’s a beautiful interctive chart. Let’s go ahead and see a few more examples of something bigger than a simple bar chart.
Building Interactive Heatmap / Correlation Plot
Let’s try to visualize a Heatmap (of Correlation Plot) of numeric columns of
mtcars dataset. To do that, we’ve to first select the numeric columns which we’ll do with
select_if(is.numeric) and then we’ve to build the correlation matrix which the base-R function
cor() does it smoothly.
Now that we’ve got a
matrix let’s convert it to a
data.frame and for us to draw a heatmap – we need 3 things primarily:
- x-axis – categorical
- y-axis – categorical
- fill value – continuous
So, we’ll convert the rownames of the resultant dataframe to a column and then convert the wide format data into long format using
At this point our data is in the desirable format for a heatmap. Simply for aesthetics improvement, let’s round off the correlation values.
Finally, we’ll use our
apex() function with
type = 'heatmap' that gives us a color-filled heatmap (that’s also interactive).
library(apexcharter) library(tidyverse) mtcars %>% select_if(is.numeric) %>% cor() %>% as.data.frame() %>% rownames_to_column("col") %>% pivot_longer(cols = -col, names_to = "type") %>% mutate(value = round(value,2)) %>% apex(type = "heatmap", mapping = aes(x = col, y = type, fill = value))
Building Interactive Time-Series (Line) Graph
If there’s a plot where Interactive Charts are incredibly valuable, I think it’s Time-Series Graph where labelling on traditional (static) chart would sometimes make the chart clunky and less readable.
Let’s build an Interactive Time-series plot with the
apexcharter library. As you can see below, all it takes is a dataframe with a column denoting the
time field and another column with the actual
value for that time.
library(apexcharter) df <- data.frame(Y=as.matrix(EuStockMarkets), date=time(EuStockMarkets)) df %>% apex(type = "line", mapping = aes(x = date, y = Y.DAX))
Thus, We learnt how to build interactive charts using
apexcharter that follows a very minimal API similar to